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    Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data

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    16
    Author
    Yuen, HP; Mackinnon, A
    Date
    2016-10-19
    Source Title
    PeerJ
    Publisher
    PEERJ INC
    University of Melbourne Author/s
    Yuen, Hok; MacKinnon, Andrew
    Affiliation
    Centre for Youth Mental Health
    Melbourne School of Population and Global Health
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Yuen, H. P. & Mackinnon, A. (2016). Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data. PEERJ, 4 (10), https://doi.org/10.7717/peerj.2582.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260568
    DOI
    10.7717/peerj.2582
    Abstract
    Joint modelling has emerged to be a potential tool to analyse data with a time-to-event outcome and longitudinal measurements collected over a series of time points. Joint modelling involves the simultaneous modelling of the two components, namely the time-to-event component and the longitudinal component. The main challenges of joint modelling are the mathematical and computational complexity. Recent advances in joint modelling have seen the emergence of several software packages which have implemented some of the computational requirements to run joint models. These packages have opened the door for more routine use of joint modelling. Through simulations and real data based on transition to psychosis research, we compared joint model analysis of time-to-event outcome with the conventional Cox regression analysis. We also compared a number of packages for fitting joint models. Our results suggest that joint modelling do have advantages over conventional analysis despite its potential complexity. Our results also suggest that the results of analyses may depend on how the methodology is implemented.

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